scholarly journals Absolute Scaling of Single-Cell Transcriptomes Reveals Pervasive Hypertranscription in Adult Stem and Progenitor Cells

2021 ◽  
Author(s):  
Yun-Kyo Kim ◽  
Miguel Ramalho-Santos
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Ribosome Biogenesis ◽  
Progenitor Cell ◽  
Dynamic Range ◽  
Tissue Injury ◽  
Embryonic Stem ◽  
State Transitions ◽  
Single Cell Rna Sequencing ◽  
Transcriptional Output

Hypertranscription facilitates biosynthetically demanding cellular state transitions through global upregulation of the nascent transcriptome. Despite its potential widespread relevance, documented examples of hypertranscription remain few and limited predominantly to early development. This limitation is in large part due to the fact that modern sequencing approaches, including single-cell RNA sequencing (scRNA-seq), generally assume similar levels of transcriptional output per cell. Here, we use molecule counting and spike-in normalization to develop absolute scaling of single-cell RNA sequencing data. Absolute scaling enables an estimation of total transcript abundances per cell, which we validate in embryonic stem cell (ESC) and germline data and apply to adult mouse organs at steady-state or during regeneration. The results reveal a remarkable dynamic range in transcriptional output among adult cell types. We find that many different multipotent stem and progenitor cell populations are in a state of hypertranscription, including in the hematopoietic system, intestine and skin. Hypertranscription marks cells with multilineage potential in adult organs, is redeployed in conditions of tissue injury, and can precede by 1-2 days bursts of proliferation during regeneration. In addition to the association between hypertranscription and the stem/progenitor cell state, we dissect the relationship between transcriptional output and cell cycle, ploidy and secretory behavior. Our analyses reveal a common set of molecular pathways associated with hypertranscription across adult organs, including chromatin remodeling, DNA repair, ribosome biogenesis and translation. Our findings introduce an approach towards maximizing single-cell RNA-seq profiling. By applying this methodology across a diverse collection of cell states and contexts, we put forth hypertranscription as a general and dynamic cellular program that is pervasively employed during development, organ maintenance and regeneration. 

scholarly journals Massively parallel, time-resolved single-cell RNA sequencing with scNT-Seq

2019 ◽  
Author(s):  
Qi Qiu ◽  
Peng Hu ◽  
Kiya W. Govek ◽  
Pablo G. Camara ◽  
Hao Wu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Temporal Dynamics ◽  
Embryonic Stem ◽  
Droplet Microfluidics ◽  
Massively Parallel ◽  
Specific Gene ◽  
Time Resolved ◽  
Single Cell Rna Sequencing ◽  
Rna Biogenesis

ABSTRACTSingle-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell new transcript tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking metabolically labeled newly-transcribed RNAs with T-to-C substitutions. By simultaneously measuring new and old transcriptomes, scNT-Seq reveals neuronal subtype-specific gene regulatory networks and time-resolved RNA trajectories in response to brief (minutes) versus sustained (hours) neuronal activation. Integrating scNT-Seq with genetic perturbation reveals that DNA methylcytosine dioxygenases may inhibit stepwise transition from pluripotent embryonic stem cell state to intermediate and totipotent two-cell-embryo-like (2C-like) states by promoting global RNA biogenesis. Furthermore, pulse-chase scNT-Seq enables transcriptome-wide measurements of RNA stability in rare 2C-like cells. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.

scholarly journals I-Impute: a self-consistent method to impute single cell RNA sequencing data

BMC Genomics ◽  
2020 ◽  
Vol 21 (S10) ◽  
Author(s):  
Xikang Feng ◽  
Lingxi Chen ◽  
Zishuai Wang ◽  
Shuai Cheng Li
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Embryonic Stem ◽  
The State ◽  
Cell Subpopulation ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Self Consistent ◽  
Consistent Method ◽  
Self Consistency

Abstract Background Single-cell RNA-sequencing (scRNA-seq) is becoming indispensable in the study of cell-specific transcriptomes. However, in scRNA-seq techniques, only a small fraction of the genes are captured due to “dropout” events. These dropout events require intensive treatment when analyzing scRNA-seq data. For example, imputation tools have been proposed to estimate dropout events and de-noise data. The performance of these imputation tools are often evaluated, or fine-tuned, using various clustering criteria based on ground-truth cell subgroup labels. This limits their effectiveness in the cases where we lack cell subgroup knowledge. We consider an alternative strategy which requires the imputation to follow a “self-consistency” principle; that is, the imputation process is to refine its results until there is no internal inconsistency or dropouts from the data. Results We propose the use of “self-consistency” as a main criteria in performing imputation. To demonstrate this principle we devised I-Impute, a “self-consistent” method, to impute scRNA-seq data. I-Impute optimizes continuous similarities and dropout probabilities, in iterative refinements until a self-consistent imputation is reached. On the in silico data sets, I-Impute exhibited the highest Pearson correlations for different dropout rates consistently compared with the state-of-art methods SAVER and scImpute. Furthermore, we collected three wetlab datasets, mouse bladder cells dataset, embryonic stem cells dataset, and aortic leukocyte cells dataset, to evaluate the tools. I-Impute exhibited feasible cell subpopulation discovery efficacy on all the three datasets. It achieves the highest clustering accuracy compared with SAVER and scImpute. Conclusions A strategy based on “self-consistency”, captured through our method, I-Impute, gave imputation results better than the state-of-the-art tools. Source code of I-Impute can be accessed at https://github.com/xikanfeng2/I-Impute.

scholarly journals Tailoring the resolution of single-cell RNA sequencing for primary cytotoxic T cells

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kristiyan Kanev ◽  
Patrick Roelli ◽  
Ming Wu ◽  
Christine Wurmser ◽  
Mauro Delorenzi ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Performance ◽  
Dynamic Range ◽  
Single Cell Analysis ◽  
Cytotoxic T Cells ◽  
High Throughput Analysis ◽  
Specific Expression ◽  
Minute Amount ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing in principle offers unique opportunities to improve the efficacy of contemporary T-cell based immunotherapy against cancer. The use of high-quality single-cell data will aid our incomplete understanding of molecular programs determining the differentiation and functional heterogeneity of cytotoxic T lymphocytes (CTLs), allowing for optimal therapeutic design. So far, a major obstacle to high depth single-cell analysis of CTLs is the minute amount of RNA available, leading to low capturing efficacy. Here, to overcome this, we tailor a droplet-based approach for high-throughput analysis (tDrop-seq) and a plate-based method for high-performance in-depth CTL analysis (tSCRB-seq). The latter gives, on average, a 15-fold higher number of captured transcripts per gene compared to droplet-based technologies. The improved dynamic range of gene detection gives tSCRB-seq an edge in resolution sensitive downstream applications such as graded high confidence gene expression measurements and cluster characterization. We demonstrate the power of tSCRB-seq by revealing the subpopulation-specific expression of co-inhibitory and co-stimulatory receptor targets of key importance for immunotherapy.

scholarly journals Single Cell RNA Sequencing of stem cell-derived retinal ganglion cells

2017 ◽  
Author(s):  
Maciej Daniszewski ◽  
Anne Senabouth ◽  
Quan Nguyen ◽  
Duncan E. Crombie ◽  
Samuel W. Lukowski ◽  
...  
Keyword(s):  
Stem Cell ◽  
Embryonic Stem Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Retinal Ganglion Cells ◽  
Human Embryonic Stem Cell ◽  
Ganglion Cells ◽  
Embryonic Stem ◽  
Retinal Ganglion ◽  
Single Cell Rna Sequencing

ABSTRACTWe used human embryonic stem cell-derived retinal ganglion cells (RGCs) to characterize the transcriptome of 1,174 cells at the single cell level. The human embryonic stem cell line BRN3B-mCherry A81-H7 was differentiated to RGCs using a guided differentiation approach. Cells were harvested at day 36 and subsequently prepared for single cell RNA sequencing. Our data indicates the presence of three distinct subpopulations of cells, with various degrees of maturity. One cluster of 288 cells upregulated genes involved in axon guidance together with semaphorin interactions, cell-extracellular matrix interactions and ECM proteoglycans, suggestive of a more mature phenotype.

scholarly journals PISCES: A pipeline for the Systematic, Protein Activity-based Analysis of Single Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Lukas J Vlahos ◽  
Aleksandar Obradovic ◽  
Pasquale Laise ◽  
Jeremy Worley ◽  
Xiangtian Tan ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Dropout Rate ◽  
State Transitions ◽  
Specific Gene ◽  
Dramatic Improvement ◽  
Sequencing Data ◽  
Protein Activity ◽  
Cell State ◽  
Single Cell Rna Sequencing

While single-cell RNA sequencing provides a new window on physiologic and pathologic tissue biology and heterogeneity, it suffers from low signal-to-noise ratio and a high dropout rate at the individual gene level, thus challenging quantitative analyses. To address this problem, we introduce PISCES (Protein-activity Inference for Single Cell Studies), an integrated analytical framework for the protein activity-based analysis of single cell subpopulations. PISCES leverages the assembly of lineage-specific gene regulatory networks, to accurately measure activity of each protein based on the expression its transcriptional targets (regulon), using the ARACNe and metaVIPER algorithms, respectively. It implements novel analytical and visualization functions, including activity-based cluster analysis, identification of cell state repertoires, and elucidation of master regulators of cell state and cell state transitions, with full interoperability with Seurat's single-cell data format. Accuracy and reproducibility assessment, via technical and biological validation assays and by assessing concordance with antibody and CITE-Seq-based measurements, show dramatic improvement in the ability to identify rare subpopulations and to assess activity of key lineage markers, compared to gene expression analysis.

scholarly journals Single-cell RNA-seq of human induced pluripotent stem cells reveals cellular heterogeneity and cell state transitions between subpopulations

2017 ◽  
Author(s):  
Quan H. Nguyen ◽  
Samuel W. Lukowski ◽  
Han Sheng Chiu ◽  
Anne Senabouth ◽  
Timothy J. C. Bruxner ◽  
...  
Keyword(s):  
Stem Cells ◽  
Induced Pluripotent Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Pluripotent Stem Cells ◽  
Single Cells ◽  
State Transitions ◽  
Transcriptional Level ◽  
Single Cell Rna Sequencing ◽  
Induced Pluripotent

AbstractHeterogeneity of cell states represented in pluripotent cultures have not been described at the transcriptional level. Since gene expression is highly heterogeneous between cells, single-cell RNA sequencing can be used to identify how individual pluripotent cells function. Here, we present results from the analysis of single-cell RNA sequencing data from 18,787 individual WTC CRISPRi human induced pluripotent stem cells. We developed an unsupervised clustering method, and through this identified four subpopulations distinguishable on the basis of their pluripotent state including: a core pluripotent population (48.3%), proliferative (47.8%), early-primed for differentiation (2.8%) and late-primed for differentiation (1.1%). For each subpopulation we were able to identify the genes and pathways that define differences in pluripotent cell states. Our method identified four transcriptionally distinct predictor gene sets comprised of 165 unique genes that denote the specific pluripotency states; and using these sets, we developed a multigenic machine learning prediction method to accurately classify single cells into each of the subpopulations. Compared against a set of established pluripotency markers, our method increases prediction accuracy by 10%, specificity by 20%, and explains a substantially larger proportion of deviance (up to 3-fold) from the prediction model. Finally, we developed an innovative method to predict cells transitioning between subpopulations, and support our conclusions with results from two orthogonal pseudotime trajectory methods.

scholarly journals On the discovery of subpopulation-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data

2019 ◽  
Author(s):  
Helena L. Crowell ◽  
Charlotte Soneson ◽  
Pierre-Luc Germain ◽  
Daniela Calini ◽  
Ludovic Collin ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
R Package ◽  
State Transitions ◽  
Sequencing Data ◽  
Statistical Framework ◽  
Single Cell Rna Sequencing ◽  
Cell Subpopulations ◽  
Multiple Samples

AbstractSingle-cell RNA sequencing (scRNA-seq) has quickly become an empowering technology to profile the transcriptomes of individual cells on a large scale. Many early analyses of differential expression have aimed at identifying differences between subpopulations, and thus are focused on finding subpopulation markers either in a single sample or across multiple samples. More generally, such methods can compare expression levels in multiple sets of cells, thus leading to cross-condition analyses. However, given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis. For example, one could investigate the condition-specific responses of cell subpopulations measured from patients from each condition; however, it is not clear which statistical framework best handles this situation. In this work, we surveyed the methods available to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated “pseudobulk” data. We developed a flexible simulation platform that mimics both single and multi-sample scRNA-seq data and provide robust tools for multi-condition analysis within the muscat R package.

scholarly journals I-Impute: a self-consistent method to impute single cell RNA sequencing data

2019 ◽  
Author(s):  
Xikang Feng ◽  
Lingxi Chen ◽  
Zishuai Wang ◽  
Shuai Cheng Li
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Embryonic Stem ◽  
Ground Truth ◽  
The Self ◽  
Cell Subpopulation ◽  
Single Cell Rna Sequencing ◽  
Self Consistent ◽  
Consistent Method ◽  
Self Consistency

Single-cell RNA-sequencing (scRNA-seq) is essential for the study of cell-specific transcriptome landscapes. The scRNA-seq techniques capture merely a small fraction of the gene due to “dropout” events. When analyzing with scRNA-seq data, the dropout events receive intensive attentions. Imputation tools are proposed to estimate the values of the dropout events and de-noise the data. To evaluate the imputation tools, researchers have developed different clustering criteria by incorporating the ground-truth cell subgroup labels. There lack measurements without cell subgroup knowledge. A reliable imputation tool should follow the “self-consistency” principle; that is, the tool reports the results only if it finds no further errors or dropouts from the data. Here, we propose “self-consistency” as an explicit evaluation criterion; also, we propose I-Impute, a “self-consistent” method, to impute scRNA-seq data. I-Impute lever-ages continuous similarities and dropout probabilities and refines the data iteratively to make the final output self-consistent. On the in silico data sets, I-Impute exhibited the highest Pearson correlations for different dropout rates consistently compared with the state-of-art methods SAVER and scImpute. On the datasets of 90.87%, 70.98% and 56.65% zero rates, I-Impute exhibited the correlations as 0.78, 0.90, and 0.94, respectively, between ground truth entries and predicted values, while SAVER exhibited the correlations as 0.58, 0.79 and 0.88, respectively and scImpute exhibited correlations as 0.65, 0.86, and 0.93, respectively. Furthermore, we collected three wetlab datasets, mouse bladder cells dataset, embryonic stem cells dataset, and aortic leukocyte cells dataset, to evaluate the tools. I-Impute exhibited feasible cell subpopulation discovery efficacy on all the three datasets. It achieves the highest clustering accuracy compared with SAVER and scImpute; that is, I-Impute displayed the adjusted Rand indices of the three datasets as 0.61, 0.7, 0.52, which improved the indices of SAVER by 0.01 to 0.17, and improved the indices of scImpute by 0.19 to 0.4. Also, I-impute promoted normalized mutual information of the three datasets by 0.01 to 0.09 comparing with SAVER, and by 0.15 to 0.34 comparing with scImpute. I-Impute exhibits robust imputation ability and follows the “self-consistency” principle. It offers perspicacity to uncover the underlying cell subtypes in real scRNA-Seq data. Source code of I-Impute can be accessed at https://github.com/xikanfeng2/I-Impute.

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